Climate Driver Influences on Prediction of the Australian Fire Behaviour Index

Abstract

Fire danger poses a pressing threat to ecosystems and societies worldwide. Adequate preparation and forewarning can help reduce these threats, but these rely on accurate prediction of extreme fire danger. With the knowledge that climatic conditions contribute heavily to overall fire danger, this study evaluates the skill with which episodes of extreme fire danger in Australia can be predicted from the activity of large-scale climate driver patterns. An extremal dependence index for extreme events is used to depict the historical predictive skill of the Australian Bureau of Meteorology’s subseasonal climate prediction system in replicating known relationships between the probability of top-decile fire danger and climate driver states at a lead time of 2–3 weeks. Results demonstrate that the El Niño Southern Oscillation, Southern Annular Mode, persistent modes of atmospheric blocking, Indian Ocean Dipole and Madden-Julian Oscillation are all key for contributing to predictability of fire danger forecasts in different regions during critical fire danger periods. Northwest Australia is found to be particularly predictable, with the highest mean index differences (>0.50) when certain climate drivers are active, compared with the climatological index mean. This integrated approach offers a valuable resource for decision-making in fire-prone regions, providing greater confidence to users relying on fire danger outlooks for key management decisions, such as those involved in the sectors of national park and forest estate management, agriculture, emergency services, health and energy. Furthermore, the results highlight strengths and weaknesses in both the Australian Fire Danger Rating System and the operational climate model, contributing additional information for improving and refining future iterations of these systems.

A Statistical Forecasting Model for Extremes of the Fire Behaviour Index in Australia

“…Non-linear and particularly machine-learning approaches can hide or obscure the nature of the relationships used to make predictions, thereby making a forecast that is not only unable to be checked for logic, but which also cannot be learnt from. Considering these respective merits and drawbacks, as well as the strong ability of simple techniques to demonstrate relationships between fire danger and climate processes [19,20], which are well replicated by more advanced dynamical systems [19,43], we use climate driver indices to develop a multiple logistic regression model for forecasting cases of extreme fire danger based on climate driver activity. The application of this model is demonstrated through three case studies covering recent impactful fire events in Australia-the Canberra bushfires of 2003, the Black Saturday bushfires of 2009, and the Pinery fire of 2015.…”

“…For regions such as northern and central Australia, where fires are most active or destructive during other months of the year [111], the model structure could be compromised. However, as the probability of extreme fire danger in northern regions of Australia is generally highly predictable from climate activity [43], this is unlikely to introduce significant errors or weaknesses.…”